The set of microbes in our gut (i.e., our microbiota) as well as the food we ingest, both represent environmental perturbations to our physiology. The abundance of each species in our gut is dramatically altered as we consume different foods, while the chemical composition of these of foods is likewise uniquely modified in our intestines by the specific metabolic capabilities of the group of microbial organisms that resides there. Thus our diets, our microbiota, and our physiology represent a unique and complex system of interacting genes, organisms, and nutrients. Computational modeling provides a series of tools to understand and control complex systems; it is in this vein that our lab continues to develop statistical models to understand the relationship between our diet, our microbiota, and our health. Towards these long-term goals, this application has two specific aims focused on predictive models involving six defined human-derived microbiotas isolated from different individuals. Aim 1 - Building on our previous success in using gnotobiotic mice harboring a defined community of 10 human-gut bacteria to predict the abundance of each species in response to specific ingredient perturbations to the host diet, we aim to understand the quantitative relationship between our diets and our gut microbes by modeling the responses of six defined microbiota, isolated from different human donors and transplanted into gnotobiotic mice, to diet perturbations of eight commonly eaten human foods. These models will provide insight into the interpersonal variations governing the rules between diet and the microbiota (e.g., does an Escherichia coli species isolated from two different donors response to similar ways to the same food). In addition, Aim 1 will provide valuable preliminary data about the relationship between diet and the microbiota that will be used to optimize the design of Aim 2 where we will define the interrelationship between diet, the gut microbiota, and host transcriptional variation. By systematically perturbing the host diet in the context of germ-free animals and animals harboring the defined microbiotas from Aim 1, we plan to model and quantify the transcriptional changes in the host small intestine, colon, and liver that are influenced by specific diet ingredients, the microbiota, or a combination of diet and the microbiota.